Systems and methods for generating enhanced data metrics

ABSTRACT

A system and method for isolating electronic data and generating enhanced data. A data subscription unit receives data having plural data formats from data source devices. The data may be received pre-filtered or filtered by the data subscription unit. A modeling unit may receive the filtered data from the storage databases, may determine eligibility of the stored data based on eligibility criteria, sort the eligible data based on at least one sorting parameter, adjust and/or normalize the eligible data based on at least one adjustment parameter and/or an aggregate value for a data attribute, generate data metrics over a defined set of time periods based on the eligible data, generate further data metrics based on the data metrics, and derive specified values from these metrics. A data distribution device may transmit or make available the data metrics, further data metrics and values to remote devices.

TECHNICAL FIELD

The present disclosure generally relates to improving data structuremanagement and, in particular, to data structure management systems andmethods for data isolation with improved accuracy for the creation ofdata metrics.

BACKGROUND

Problems exist in the field of electronic data conversion anddistribution. Users of products, systems, processes or instruments whichseek to represent, reflect or measure underlying data types/data setsthat are complex or are difficult to analyze, or data types/data setswith low (e.g., sparse) or concentrated underlying electronic data, ordata types/data sets with underlying data that are difficult to accessor analyze, may seek additional information in order to analyze orotherwise utilize these data types/data sets. One use of electronic data(e.g., input data) is in the creation of data metrics (or otherstatistical analyses/applications) for those data types/data sets thatare complex or difficult to analyze, having sparse and/or concentratedunderlying electronic data or with underlying data that are difficult toaccess or analyze. Because the underlying electronic data issparse/concentrated, or difficult to access or analyze, or because thedata types/data sets are complex or difficult to analyze, it may bedifficult to isolate and analyze the correct underlying data, and togenerate accurate data metrics.

In the absence of sufficient data and information, and the correctanalysis and processing, conventional metrics (based on thesparse/concentrated data and information) are often inaccurate andunreliable, or no appropriate or pertinent conventional metric mayexist. Accordingly, there is a need for improved data conversion anddistribution systems which are able to isolate correct data and generateaccurate and pertinent metrics, even if the underlying data being usedis sparse and/or concentrated, or difficult to access or analyze, or ifthe data types/data sets being measured are complex or are difficult toanalyze.

SUMMARY

A system and method for isolating electronic data and generatingenhanced data is disclosed. A data subscription unit may receive datahaving a plurality of data formats from data source devices. In anexample, the data may be filtered by one or more among the data sourcedevices. In another example, the data subscription unit may filter thereceived data based on filter criteria. In another example at least aportion of the data may be filtered by one or more among the datasources and another portion of the data may be filtered by the datasubscription unit based on the filter criteria. The data subscriptionunit may transmit the filtered data to storage databases. A modelingunit may receive the filtered data from the storage databases, determineeligibility of the stored data based on eligibility criteria, sort theeligible data based on at least one sorting parameter, and/or adjust theeligible data based on at least one adjustment parameter, generate datametrics over a defined set of time periods based on the eligible sortedand adjusted data, generate further data metrics based on the datametrics, and and/or derive specified values from among the data metricsand the further data metrics. A data distribution device may transmit ormake available the data metrics, the further data metrics and values toremote devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an example data structuremanagement system for data isolation and generation of improved datametrics (“enhanced data”) according to the present disclosure.

FIG. 2 is a functional block diagram of an example data subscriptionunit according to the present disclosure.

FIG. 3 is a functional block diagram of an example modeling unitaccording to the present disclosure.

FIG. 4 is a functional block diagram of an example data distributiondevice according to the present disclosure.

FIG. 5 is a functional block diagram of one or more example remotedevices according to the present disclosure.

FIG. 6 is a flowchart illustrating an exemplary process for generating ayield curve using the data structure management system of the presentdisclosure.

FIG. 7 is chart illustrating a first bank yield curve according to thepresent disclosure from which bank yield index (BYI) values may betaken.

FIG. 8 is a chart illustrating how an implied credit spread is derivedfor a transaction, based on its vertical distance from a term risk freerate (e.g., the Secured Overnight Financing Rate (SOFR)) yield curve forthe same day.

FIG. 9 is a chart illustrating how an implied credit spread curve isconstructed based upon the implied credit spreads.

FIG. 10 is a chart illustrating a second bank yield curve constructedfrom an implied credit spread curve and a term risk free rate (e.g.,SOFR) curve according to the present disclosure from which BYI valuesmay be taken.

FIG. 11 is a functional block diagram illustrating an example computersystem according to the present disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to systems, methods andnon-transitory computer-readable storage media for data isolation andthe creation of data metrics such as a credit spread yield curve, a termrisk free rate yield curve, a bank yield curve, a bank yield index(BYI), etc.

An example data structure management system according to the presentdisclosure may include at least one data subscription unit and at leastone modeling unit. The data subscription unit may have at least one datainterface communicatively coupled to a plurality of data source devicesand may be configured to receive or pull data having a plurality of dataformats from the plurality of different data source devices during oneor more particular time periods (e.g., one or more days in a pre-definedcollection period). In an example, the data having a plurality of dataformats may be filtered by one or more among the plurality of differentdata sources prior to transmission to the data subscription unit. Inanother example, the data subscription unit may be configured to filterthe data upon receipt. In yet another example, a portion of the data maybe filtered by at least one of the data sources and the same or anotherportion may be filtered or further filtered by the data subscriptionunit. The data having a plurality of data formats may be filtered basedon predefined parameters or criteria (e.g., transaction and/or valuedate, transaction and/or contract type, etc.). The data subscriptionunit may transmit the data to one or more storage databases via securecommunication channel(s) over a network. The modeling unit of the datastructure management system may include one or more servers, anon-transitory memory, and one or more processors including machinereadable instructions. The modeling unit may be communicatively coupledto the data subscription unit. The modeling unit may also include a datareceiver module, a data verifier, a data sorting module, a dataadjustment module, a data processing module, and/or a data transmissionmodule. The data receiver module of the modeling unit may be configuredto receive filtered data from the one or more storage databases via thesecure communication channel(s) over the network. The data verifier ofthe modeling unit may be configured to apply one or more eligibilitycriteria to the filtered data. The data sorting module may be configuredto sort the eligible data into one or more groups (e.g., by transactiondate, value data, and/or tenor) based on one or more parameters. Theeligible sorted data may be adjusted by the data adjustment module toreflect, for example, changes in market rates over relevant dates and/orto convert between different price/yield quotation conventions.

The data processing module may use the eligible sorted and possiblyadjusted data to generate accurate data metrics. In one example, thedata metrics may comprise a bank yield curve over a defined set of timeperiods (e.g., a bank yield curve over maturities up to one year) andone or more forward-looking term rates may be determined from the bankyield curve. This bank yield curve may be constructed directly based onbank funding and bond transaction data. In another example, the datametrics may comprise a term nearly risk-free rate curve constructed fromrisk free rate and derivatives data and a bank credit spread yield curveconstructed from bank funding and bond transaction data and therisk-free rate yield curve, which may be combined to produce a bankyield curve over a defined set of time periods. The data transmissionmodule may be configured to transmit the metrics, which may include oneor more of a bank yield curve, a bank credit spread yield curve and aterm nearly risk-free rate curve, and one or more values (e.g.,forward-looking term rates in the case of a BYI, etc.) determined fromthese metrics to a data distribution device via one or more securecommunication channels over a network. It should be noted that the term“risk-free” may be used for the term “nearly risk-free,” and vice versa,in the following description.

The data distribution device may further include a non-transitory memoryand at least one data distribution interface. The non-transitory memorymay be configured to store the data metrics (e.g., at least one of thebank yield curve, a bank credit spread yield curve, and a term nearlyrisk-free rate curve, and the one or more values or rates, such as theBYI, determined from these metrics). One or more of the datadistribution interfaces may be configured to provide securecommunications with at least one of one or more remote devices.

A remote device may include a non-transitory memory, one or moreprocessors including machine readable instructions, a data distributionreceiver interface communicatively coupled to the data distributiondevice, a user information interface, a market data source interface,and/or a user display interface. One or more of the remote devices maybe further configured to receive the data metrics from the datadistribution device via the data distribution receiver interface,receive user input data via the user information interface, generatesupplementary projected data via one or more processors, and/or displayat least a portion of the projected data and the supplementary projecteddata on a user display interface. The supplementary projected data maybe based on the data metrics' sensitivities, projected data, user inputdata, and/or current external (e.g., market) data.

Referring now to FIG. 1, a functional block diagram of an example datastructure management system 100 for data isolation and generation ofimproved data metrics (“enhanced data”) according to the presentdisclosure is shown. The data structure management system 100 mayinclude a data subscription unit 101, a modeling unit 103, and a datadistribution device 105. The data subscription unit 101, the modelingunit 103 and the data distribution device 105 may be communicativelycoupled via a network 108. Alternatively or additionally, the datasubscription unit 101 may be directly coupled to the modeling unit 103,and/or the modeling unit 103 may be directly coupled to the datadistribution device 105, without the use of a network.

The data structure management system 100 may be communicatively coupledto one or more remote devices 107 via a network 106. In one example,each of the remote devices 107 may be used by participants including forexample, data managers, data analysts, regulatory compliance teams, andthe like. In an example, the data structure management system 100 mayinclude a surveillance module (not shown) for supervision and/orsurveillance of the modeling unit 103. The surveillance may be performedpost-publication (e.g., after the data metrics are distributed to theone or more remote devices 107) to verify input data is correct and/orto check for potential manipulation of the input data. Although the datastructure management system 100 is described in some examples below withrespect to data classes associated with electronic instrument data, thedata structure management system 100 may be used with any electronicdata classes associated with any type of electronic data, includingthose having sparse data. Some non-limiting examples include trafficpattern data, population distribution data, galactic activity data, etc.

The data subscription unit 101 may have at least one data interfacecommunicatively coupled to one or more data source devices 109. Althoughthe description and drawings herein describe the data conversion anddistribution system 100 and its surrounding environment as having one ormore data source devices 109 and one or more remote devices 107, in someexamples, there may be any combination of data source devices 109 and/orremote devices 107, including for example, a single data source device109 and a single remote device 107, or a single data source device 109and no remote devices 107. One or more of the one or more data sourcedevices 109, data subscription unit 101, modeling unit 103, datadistribution device 105, and one or more remote devices 107 may includeone or more computing devices including a non-transitory memorycomponent storing computer-readable instructions executable by aprocessing device to perform the functions described herein.

The one or more data source devices 109 may be communicatively coupledto the data subscription unit 101 via a network 110. The datadistribution device 105 may be communicatively coupled to the one ormore remote devices 107 via the network 106. The networks 110 and 106may include two or more separate networks to provide additional securityto the one or more remote devices 107 by preventing direct communicationbetween the one or more remote devices 107 and the one or more datasource devices 109. Alternatively, the networks 110, 106 may be linkedand/or a single large network. The networks 110, 106 (as well as network108) may include, for example, a private network (e.g., a local areanetwork (LAN), a wide area network (WAN), intranet, etc.) and/or apublic network (e.g., the internet). The networks 110 and/or 106 may beseparate from or connected to network 108.

In an example implementation, the one or more data source devices 109may include data sources 154-172. It should be noted that only datasources 154, 156 and 172 are shown in FIG. 1 for illustrative purposes.A first data source data source 154 may include bank funding transactiondata received from submitting banks, for example, through secure filetransfer, or from another appropriate data source. A second data source156 may include one or more suitable data providers of informationregarding one or more business day calendars (e.g., a business daycalendar for the United States, a business day calendar for the UnitedKingdom, etc.). A third data source 158 (not shown) may include one ormore suitable data providers of one or more reference rates. A fourthdata source 160 (not shown) may include one or more suitable dataproviders of bond transaction data. In some examples, the fourth datasource 160 (not shown) may include one or more trade repositories, oneor more trading venues, other suitable trade reporting service(s) and/orplatform(s). A fifth data source 162 (not shown) may include one or moresuitable data providers of additional transaction data that may berelevant to bank yields such as, without being limited to, certificatesof deposit (CD), commercial paper (CP) data, etc. In some examples, thefifth data source 162 (not shown) may include one or more traderepositories, one or more trading venues, other trade reportingservice(s) and/or platform(s). A sixth data source 164 (not shown) mayinclude one or more suitable data providers of other (e.g., additional)market data appropriate to processing input, rate and yield data (e.g.,that may be relevant to processing input, rate and yield data). Aseventh data source 166 (not shown) may include one or more dataproviders of suitable eligibility criteria appropriate to filteringreference rate, contract, transaction, yield and other appropriate inputdata. An eighth data source 168 (not shown) may include one or moresuitable data providers of data from derivative transactions referencingnearly risk-free reference rates. In some examples, a ninth data source170 may include one or more trade repositories, one or more tradingvenues, other trade reporting service(s) and/or platform(s). A tenthdata source 172 may include one or more suitable data providers ofinformation regarding expected policy rate change dates (e.g., acalendar of expected rate change dates for the United States, the UnitedKingdom, etc.). Notably, more or fewer data sources, or a combination ofdifferent types of data sources, may comprise the data structuremanagement system 100 of the present disclosure.

The data structure management system 100 may include one or morereference databases 128 and one or more storage databases 130. The oneor more reference databases 128 may store transaction, contract, rateand other input data eligibility criteria for the one or more datasource devices 109 (e.g., in respect of bond transaction data, a bondidentifier, transaction type, size, time window of execution, etc.) toassist the modeling unit 103 in processing/filtering data as describedbelow. The one or more storage databases 130 may store transaction andother input data gathered by the data subscription unit 101, asdescribed below.

In an example, the data structure management system 100 may perform oneor more of the above processes automatically. In another example, thedata management system 100 may include a surveillance module (notshown), which may be referred to as an administration/surveillancemodule (not shown). The administration/surveillance module may beconfigured to receive one or more inputs. The surveillance module mayallow supervision, administration, and/or surveillance of the datacollection, filtration/eligibility criteria application, modelling,calculation and publication process performed by the modeling unit 103.It should be noted that this administration and/or surveillance mayoccur contemporaneously with the data collection, processing,calculation and/or post-publication.

Referring now to FIG. 2, a functional block diagram of the exemplarydata subscription unit 101 is shown. The data subscription unit 101 mayinclude at least one data interface 201 communicatively coupled vianetwork 110 to the one or more data source devices 109. The datasubscription unit 101 may be configured to receive data having aplurality of data formats (e.g., via the electronic data files, viadirect data feeds, etc.) produced by the one or more data source devices109. The data subscription unit 101 may include one or more processors209 (also referred to herein as processing component 209), logic 210 anda non-transitory memory 205 including instructions 206 and space tostore subscription preferences. The subscription preferences may definethe parameters of the communicative coupling between the datasubscription unit 101 and the one or more data source devices 109. Inother words, the subscription preferences may define which of the one ormore data source devices 109 to connect to and communicate with, thetype, volume and/or frequency with which data is pulled or received fromsaid data source devices 109, and/or any other parameters related to theflow of data and information. The data subscription unit 101 may alsoinclude a data transmitter 207 configured to transmit the received data(having the plurality of data formats) via secure communicationchannel(s) over the network 108. Transmissions from the data transmitter207 may be stored in the one or more storage databases 130, where it maybe accessed by the modeling unit 103 of the data structure managementsystem 100.

The data subscription unit 101 may, for example, via processor 209,receive subscription preferences, store the received subscriptionpreferences in the non-transitory memory 205, and communicatively couplevia the at least one data interface 201 of the data subscription unit101 to the one or more data source devices 109. Communicatively couplingvia the at least one data interface 201 of the data subscription unit101 to the one or more data source devices 109 may further includesending a request (from the data subscription unit 101) to the one ormore data source devices 109 to receive data (e.g., files, feeds, etc.)related to a particular input or data, over a particular communicationlink, at a specified frequency. The data subscription unit 101 may thenconnect to the one or more data source devices 109 by establishing acommunication link between the data interface 201 of the datasubscription unit 101 and the one or more data source devices 109 viathe network 110. The network 110 may be unsecured or secured and wiredand/or wireless.

The data subscription unit 101 may be subscribed to the one or more datasource devices 109 if a request transmitted to at least one data source(e.g., data sources 154-172) is accepted and data and information istransmitted in accordance with the request from the at least one datasource (154-172) to the data subscription unit 101 via the network 110.A request may specify the type and/or volume of data and informationrequested, the frequency at which it should be transmitted, as well asthe communication protocol that should be used to transmit the data andinformation. For example, a request may be that the one or more datasource devices 109 transmit electronic data files regarding all tradingactivity relating to an instrument or a product at the end of everybusiness day in a data collection period in accordance with a filetransfer protocol (FTP) or secure file transfer protocol (SFTP).Alternative secure communication channels or links may be utilized.

In accordance with the received request, the respective one or more datasource devices 109 may generate and/or transmit one or more electronicdata files containing the requested information (or transmit directlyvia data feed(s)) at the specified frequency. The information and datamay then be received by the data subscription unit 101 via datainterface 201. In this manner, the data structure management system 100may dictate receiving only the type and volume of data and informationthat is pertinent to supplementing and/or generating statisticalinformation (e.g., data projections and sensitivities) related to one ormore electronic data classes for which directly-related or historicalinformation is sparse or unavailable. As a result, the processing andmemory requirements of the data structure management system 100 areminimized and system efficiency is maximized (i.e., by avoidingreceiving irrelevant or voluminous data beyond what is needed ordesired), particularly in implementations where large volumes of data(e.g., millions of data requests and/or data points) may be received ina given period of time (e.g., per day).

The electronic data and information received via the at least one datainterface 201 of the data subscription unit 101 may be in a variety offormats. For example, the data file formats may correspond to thespecifications of each of the one or more data source devices 109 fromwhich the data and information are received. Additionally, the dataformats may have different data transfer parameters, compressionschemes, and the like. Furthermore, in some examples, the data contentmay correspond to different forms of data, such as different currencies,date formats, time periods, and the like. The data interface 201 mayreceive a separate electronic data feed or file for each request forinformation. For example, the data interface 201 may receive a singledata file or data packet, corresponding to one or more requests forinformation, from each of the one or more of the data source devices 109to which it subscribes.

The frequency and volume of data which is provided to the datasubscription unit 101 and the setup for a communication link may bearranged in accordance with the subscription preferences stored on thedata subscription unit 101. The subscription preferences may be providedby a user device (not shown) in communication with the data structuremanagement system 100 (either via a direct and/or remote connection todata subscription unit 101, or by way of any other input means of thedata structure management system 100) and/or remotely by a remote device107 communicating with the data structure management system 100. Thepreferences may be stored on the non-transitory memory 205 of the datasubscription unit 101. Optionally, the data received via the datainterface 201 may also be stored in the non-transitory memory 205 of thedata subscription unit 101. Newly received data from the one or moredata source devices 109 may be used to update, add to, or remove dataalready stored in the non-transitory memory 205 of the data subscriptionunit 101.

The subscription preferences may be received by a data subscriptionpreference receiver 203 specially configured to receive subscriptionpreference data, and store and/or update subscription preferences in atleast a portion of the non-transitory memory component 205 of the datasubscription unit 101.

After the one or more data source devices 109 are subscribed to by thedata subscription unit 101, the data may be automatically transmittedfrom the one or more data source devices 109 to the data subscriptionunit 101 as noted above. A predetermined event or time (e.g., the closeof a business day, a weather event, a predetermined time of day, etc.)may cause the one or more data source devices 109 to automaticallygenerate the data and/or information for the data subscription unit 101.

In an example, the data having a plurality of data formats may befiltered by one or more among the one or more data source devices 109prior to transmission to the data subscription unit 101. In anotherexample, the data subscription unit 101 may include a data filteringmodule 222. In yet another example, a portion of the data may be“pre-filtered” by data source device(s) 109 and another portion may befiltered by data filtering module 222. In yet another example, datafiltering module 222 may perform additional filtering on at least aportion of the pre-filtered data. The data filtering module 222 mayfilter the data according to one or more criteria. In one example, thedata may be filtered by one or more submission/publication days. Thedata filtering module 222 may define an input data time window (e.g.,midnight New York time on the second preceding day to midnight New Yorktime on the preceding day for each day within a designated collectionperiod) during which the data from the one or more data sources 109 isto be collected.

The data subscription unit 101 may further include one or more securityprotocols. The security protocols may include, for example, verificationof one or more unique identifiers associated with the receivedelectronic data/information, including, for example the unique data fileidentifier and/or a unique data source identifier. For example, theunique data source identifier may be utilized by the data subscriptionunit 101 to verify that it is receiving data and/or information from theappropriate one or more data source devices 109. Such a system may beadvantageous in preventing denial of service attacks and other maliciousactions which are intended to harm the data structure management system100 or the remote device(s) 107 (e.g., by way of the data structuremanagement system 100).

Referring now to FIG. 3, a functional block diagram of the modeling unit103 is shown. The modeling unit 103 may include a non-transitory memory303 storing machine readable instructions 304, and one or moreprocessors 305 (also referred to herein as processing component 305)including processor logic 306. The modeling unit 103 may becommunicatively coupled to the reference database(s) 128 and/or thestorage database(s) 130. The modeling unit 103 may also include a datareceiver module 307, a data verifier 309, a data sorting module 311, adata processing module 312, a data adjustment module 317, and/or a datatransmission module 315. Although the modeling unit 103 is illustratedin FIG. 1 as a single machine (e.g., a server), in some examples, themodeling unit 103 may include one or more servers, directly connected ina single location and/or networked across multiple locations.

The data receiver module 307 may be configured to receive electronicdata having the plurality of data formats from the one or more referencedatabases 128 and/or the one or more storage databases 130 via anoptionally secure communication channel over the network 108. Forexample, the data receiver module 307 may retrieve the filtered datastored in the one or more storage databases 130 by the data subscriptionunit 101. The data receiver module 307 may retrieve one or moreeligibility criteria stored in the one or more reference databases 128.Once the data receiver module 307 receives the data having the pluralityof data formats, it may transfer the data from the data receiver module307 to the data verifier 309.

The data verifier 309 may be configured to monitor the one or more datasource devices 109 for new and/or eligible data and to verify incomingdata from among the data sources 154-172. The verification may includecomparing the incoming data to one or more eligibility criteria storedin the one or more reference databases 128 and performing otherverification checks (e.g., for errors). The eligibility criteriaretrieved from the one or more reference databases 128 may be associatedwith a particular data source. The data verifier 122 may add/removeeligibility criteria from the one or more reference databases 128. Theaddition/removal of eligibility criteria from the one or more referencedatabases 128 may be based on data received from the one or more datasource devices 109. The data verifier 309 may proactively pull data fromthe data sources 154-172. Alternatively, data from the data sources154-172 may be pushed to the data verifier 309 at one or more times(e.g., periodically, under particular conditions, etc.).

The data verifier 309 may apply one or more eligibility criteria to, forexample, the first data source 154, such as, without being limited to, awindow of execution, a transaction size, a transaction type, etc. Thedata verifier 309 may apply one or more eligibility criteria to, forexample, bond transaction data of the fourth data source 160, such as,without being limited to, a bond identifier based on issuer, issuerinformation, bond characteristics and/or transaction characteristics.The data verifier 309 may apply one or more eligibility criteria to, forexample, the fifth data source 162, such as, without being limited to, awindow of execution, a transaction size and/or a transaction type. Itshould be noted that the one or more eligibility criteria describedabove, as well as others, may be applied to any data from one or more ofthe data sources 154-172 (including, without being limited to, data fromderivatives referencing risk free rates, risk free rate information,etc., as discussed above).

The data verifier 309 may verify whether some or all of the submitteddata from the one or more data source devices 109 meets the eligibilitycriteria upon submission to the data structure management system 100,and may perform other verification checks (e.g., for errors). In someexamples, the data verifier 309 may verify the data provided by the oneor more data source devices 109 to obtain an eligible data set. When thesubmitted data meets the eligibility criteria, the data verifier 309 maypermit the data to be processed by the data structure management system100. When the submitted data does not meet the eligibility criteria, insome examples, the data verifier 309 may permit the data to be discardedby data structure management system 100. In some examples, theeligibility criteria for the one or more data source devices 109 may bedetermined by pre-determined and stored administrator rules of the datastructure management system 100.

After checking against eligibility criteria, the data may be sorted bythe sorting module 311. The sorting module 311 may group the eligibledata into one or more groups. In one exemplary implementation, thegroups for transaction data may be tenor groups, although otherimplementations may define other groups according to the type of dataand/or industry in which the system of the present disclosure is beingimplemented. In this example, the sorting into the one or more tenorgroups may be based on one or more factors, such as (for example) daysto maturity as of the transaction execution date or the transactioneffective date. The number of the one or more tenor groups and thefactors used for sorting may be predetermined, for example,automatically or by predetermined (stored) administrator rules of thedata structure management system 100.

The eligible, sorted data may be adjusted by a data adjustment module317 to reflect, for example, changes in market rates over relevant datesand/or to convert between different price/yield quotation conventions.Each of the one or more tenor groups in this example may have anassociated minimum number and/or volume of data points. For example, atenor group may be classified by the sorting module 311 to store anumber of transactions within a particular data collection period. Ifthis tenor group has fewer than a minimum number of transactions for thedata collection period, additional transaction data from previous daysmay be added to the tenor group by the data verification module 309 orthe data sorting module 311. In an example, the data verification module309 or the data sorting module 311 may “de-duplicate” the received dataif the data sources 154-172 include multiple reports for the sametransaction (e.g., from buyer, seller, and/or dealer) with the same orslightly different details between reports.

In another example, if a minimum aggregate volume or transaction countthreshold is not reached over the course of the entire data collectionperiod (i.e. for all tenors), then additional transaction data fromprevious days may be added to the eligible set of transactions by thedata verification module 309 or the data sorting module 311. Theprevious days' transaction data may be adjusted using appropriatereference rate data (e.g., term nearly risk-free reference rates) overthe same period, or other market data.

Continuing with these examples, if any of the tenor groups or all thetenor groups together still has insufficient eligible transaction data(e.g., transaction count or transaction volume) after applying the abovelook-back protocol, a contingency policy may be applied by the dataprocessing module 312. In addition, the data adjustment module 317 mayapply a customized levelling/weighting methodology when it is determinedthat the data includes an excess of input data from a particular sourceor of a particular type (e.g., from the same issuer).

The data processing module 312 may derive data metrics (e.g., a bankyield curve, credit spread curve, or term nearly risk-free rate curve)from eligible data points, for example, using a best-fit methodology(e.g., robust regression) or an implied term-rate generation methodology(e.g. a step-function and compounding). In one example, credit spreadvalues for each eligible transaction may be derived from subtractingvalues on a term nearly risk-free rate curve from transaction yield datapoints and fitted to generate a credit spread curve. The data processingmodule 312 may further combine data metrics (e.g., a term nearlyrisk-free rate curve and a credit spread curve) to generate a bank yieldcurve. Specific values may be produced from these metrics (e.g., one ormore term rates derived from the bank yield curve at given maturities,such as a BYI). In some examples, other relevant input data, includingbusiness day calendar data, expected rate change date data, andreference rate data may be utilized at one or more steps of theprocessing of the input data.

The output from the data processing module 312 may be transmitted viathe data transmission module 315 to the data distribution device 105 viaone or more secure communications over network 108.

Referring now to FIG. 4, a functional block diagram of the exemplarydata distribution device 105 of this disclosure is shown. The datadistribution device 105 may include one or more processors 403 (alsoreferred to herein as processing component 403) including processorlogic 404. The data distribution device 105 may include at least onedata distribution receiver 405 configured to receive information fromthe modeling unit 103. The data distribution device 105 may includenon-transitory memory 401 including instructions 402 to store theoutputs from the data processing module 312.

The data distribution device 105 may include at least one datadistribution interface 407 configured to securely communicate with theone or more remote devices 107 via the network 106. The non-transitorymemory 401 of the data distribution device 105 may also be configured tostore predefined settings for the one or more remote devices 107. In anexample, the data distribution device 105 may transmit the data outputfrom the data processing module 312 to the one or more remote devices107 via email. In another example, the data distribution device 105 maypublish the data on a website via a server, etc. In yet another examplethe data distribution device 105 may transmit the data to one or moreredistributors (e.g., Bloomberg, Refinitiv, etc.).

Referring now to FIG. 5, a functional block diagram of the one or moreexemplary remote devices 107 of the present disclosure is shown. The oneor more remote devices 107 may include a non-transitory memory 501storing machine readable instructions 502, one or more processors 503(also referred to herein as processing component 503) includingprocessor logic 504, a data distribution receiver interface 505, a userinformation interface 507, and a user display interface 511. It shouldbe noted that the one or more remote devices 107 may include one or moreservers and the user information interface 507 and the user displayinterface 511 may be optional.

The data distribution receiver interface 505 may be specially configuredto be communicatively coupled to the data distribution device 105 vianetwork 106. For example, the remote device 107 may be speciallyconfigured to perform certain data processes, contain an up-to-dateversion of a web browser associated with the data structure managementsystem 100, and have an Internet connection capable of communicationwith the data structure management system 100. The remote device 107 mayhave an account with the service provider of the data structuremanagement system 100. The remote device 107, and, more specifically thedata distribution receiver interface 505, may establish a secureconnection with the data distribution device 105. The secure connectionmay be mediated by a password portal on a web-service, a securedapplication, biometrics device(s), and the like. Additional securitymeasures which allow for encrypted communications (such as industrystandard secured hypertext transfer protocol (HTTPS), secure socketlayer (SSL) certificates, and the like) may also be used. Although asingle remote device 107 is discussed, a plurality of remote devices 107may be used with the data structure management system 100.

Each remote device 107 may be configured to receive, via the datadistribution receiver interface 505, enhanced data and information. Inan example, this enhanced data and information may comprise data metricssuch as at least one of the yield curve metrics, such as the one or morevalues determined from the yield curve from the data distribution device105. The remote device 107 may also be configured to receive user inputdata via the user information interface 507. In one example, the remotedevice 107 may also be configured to generate supplementary projecteddata based on at least one of the yield curve and the one or more valuesdetermined from the yield curve.

The processing component 503 of each of the remote devices 107 and theprocessing component 403 of the data distribution device 105 may work inunison to assist the data processing module 312 generate supplementalprojected data. The data distribution device 105 may receive and storedata from the remote device 107. The stored data from the remote device107 may be accessed by the data processing module 312, which may thengenerate the supplemental projected data. The supplemental projecteddata may then be transmitted from the modeling unit 103 to the datadistribution device 105, as described above, and then to the remotedevice 107. The remote device 107 may receive and/or store thesupplementary projected data from the data distribution device 105.

The remote devices 107 may also display the enhanced data (e.g., atleast one of the yield curves and the one or more values determined fromthe yield curves) via user display interface 511. The user displayinterface 511 may further include a graphical user interface (GUI),application programming interface (API) and the like. The remote device107 may be configured to receive user graphical user interface (GUI)preference data via interface 507. Using the received user GUIpreference data, the remote device 107 may extract information including(in an example referenced above) the yield curves and the one or morevalues determined from the yield curves from the memory 501 of theremote device 107 and/or memory 401 of the data distribution device 105.The extracted information may then be displayed on the graphical userinterface of the user display interface 511 in accordance with the userGUI preference data.

Referring now to FIG. 6, a flowchart illustrating an exemplary processgenerating a yield curve using the data structure management system 100is shown. The data processing module 312 may generate a yield curve andvalues for one or more periods of time from the data received from thedata sorting module 311, as discussed above. The one or more periods oftime may correspond to the one or more of the tenor groups created bythe sorting module 311. Based on transactional and other data receivedfrom the from one or more of the data source devices 109, the yieldcurve may be generated by the data processing module 312 and distributedby the data distribution device 105 at a predetermined frequency (e.g.,daily).

Beginning with Step 602, the data subscription unit 101 may receive datafrom the one or more data source devices 109. The data may include, atleast, wholesale primary market funding transaction data and secondarymarket bond transaction data. The wholesale primary market fundingtransaction data may be received from, at least, the first data source154, and may include inter-bank deposits, institutional certificates ofdeposit, and/or commercial paper. In an example, the wholesale primarymarket funding transaction data may be received daily from one or morelarge internationally active bank systems. The secondary market bondtransaction data may be received from, at least, the fourth data source160. In an example, the secondary market bond data may be received inrespect of one or more large internationally active bank system issuers.The bank systems may be selected by, for example, pre-determinedadministrator rules of the data structure management system 100 orautomatically according to programmed logic.

The bank systems may be subject to one or more minimum criteria, such as(without limit): consolidated group assets greater than USD $250M or itsequivalent in other currencies; investment grade credit ratings at agroup parent level or at the level of the largest banking (operatingcompany) subsidiary where the parent does not have credit ratings, etc.The ratings may be provided by at least two credit rating agencies'systems that are used in US, European, and/or Asian debt capital andloan markets. Other relevant factors when selecting internationallyactive bank systems may include: a bank's current or historicalparticipation at the group parent level or by one of its subsidiaries asa submitter/contributor to one of certain interbank offered rates (e.g.,LIBOR, EURIBOR, etc.); the formation of an intermediate holding companyin the United States as a result of having greater than USD $50B inassets in the United States; the bank's presence in wholesale USDcapital, loan or money markets; and any other factors that the datastructure management system 100 may deem relevant (according topre-determined rules) over time to ensure the banking index remainsrepresentative of the economic reality it seeks to measure.

At Step 604, the data subscription unit 101 may filter the transactionalinput data based on, for example, an input data time window (e.g.,midnight New York time on the second preceding day to midnight New Yorktime on the preceding day for each day within a designated collectionperiod) during which the data from the one or more data sources 109 isto be collected. As described above, this step may be optional, in someexamples, if the one or more data source devices 109 filter the dataprior to transmission to the data subscription unit. In Step 606, thedata subscription unit 101 may store the filtered data in the one ormore storage databases 130.

Next, at Step 608, the modeling unit 103 may retrieve the filtered datafrom the one or more storage databases 130. In Step 610, the modelingunit may apply one or more eligibility criteria, stored in the one ormore reference databases 128, to the filtered data.

In an example, the criteria for eligible wholesale primary marketfunding transaction data may include a minimum funding transaction sizeof at least USD $10M and certain specified transaction types andmaturities. These criteria may be assessed to determine whether thefunding transactions are eligible for use by the data processing unit312. Table 1 shows example eligibility criteria for wholesale primarymarket funding transaction data.

TABLE 1 Example Primary Market Funding Transaction Eligibility CriteriaCategory Criteria Transaction Provider Confirmed Eligible ProviderTransaction Currency USD Transaction Size ≥USD $10M Transaction TypeUnsecured term deposits, commercial paper (fixed rate and primaryissuance), certificates of deposit (fixed rate and primary issuance)Counterparty Type Banks; Central banks; Governmental entities;Multilateral development banks; Non-bank financial institutions;Sovereign wealth funds; Supranationals; and Corporations (fortransaction maturities >35 days). Days to Maturity ≥5 business days and≤500 calendar days of Transaction

The secondary market bond transaction data may include secondary markettransactions in wholesale unsecured bonds. More specifically, thesecondary market bond transaction data may include yields for eligiblesecondary market transactions in eligible senior unsecured fixed rateUSD-denominated wholesale bonds issued by internationally active banks.The secondary market bond transaction data may be issued by one or more(e.g., 30) large internationally active banking groups. In an example,the secondary market bond transaction data may be received daily fromone or more trade reporting and compliance engines. Table 2 showsexample eligibility criteria for secondary market bond transaction data.

TABLE 2 Example Secondary Market Bond Transaction Eligibility CriteriaCategory Criteria Bond Issuer Eligible Issuer Banks Issuance CurrencyUSD Issuance Size ≥USD $500M Transaction size ≥=USD 5M Bond Type Fixedcoupon bond. No economic calls prior to 30 days before maturity CouponRange ≥1 percent and ≤5 percent, subject to adjustment over time basedupon the current interest rate environment Calendar days to maturity ofthe ≥20 and ≤500 bond at settlement of transaction

The data verifier 309 may apply one or more eligibility criteria to thewholesale primary market funding transaction data and secondary marketbond transaction data to ensure the input data is representative of theeconomic reality the yield curve or BYI is designed to measure. The oneor more eligibility criteria may include one or more of: fundingtransaction type, funding transaction counterparty, funding/bondtransaction size, bond type (e.g., coupon type and call eligibility),coupon range, days to maturity of the bond, bond issuance size,effective date of transaction relative to trade date.

In an example, the data verifier 309 may process the wholesale primarymarket funding transaction data and secondary market bond transactiondata by reference to these eligibility criteria.

At Step 612, the eligible transaction data collected within the relevantdata collection period may be sorted into one or more tenor groups bythe data sorting module 311. The sorting may be based on criteria suchas (for example) days to maturity (of the funding transaction or therelevant bond). The eligible data may be allocated into specific tenorgroups based on specified maturity ranges (e.g., for a one-month tenorgroup, the range may be 20 to 49 calendar days). Table 3 illustratesexample tenor groups from one week (1 W) to great than twelve months(>12M) and corresponding maturity ranges.

TABLE 3 Example Tenor groups From To (calendar days (calendar days TenorPeriod except where noted) except where noted) 1 W 5 (business days) 19(calendar days) 1 M  20 49 2 M  50 79 3 M  80 100 4 M 101 125 5 M 126149 6 M 150 210 7 M 211 234 8 M 235 258 9 M 259 282 10 M  283 305 11 M 306 329 12 M  330 390 >12 M   391+

Each tenor group may have a target volume (e.g., a total volumethreshold) and/or number of transactions (e.g., 10 transactions). Insome examples, overall thresholds may cover all tenor groups and/orthresholds may be applied at the tenor group level. Each data collectionperiod may also have a target aggregate volume and/or a target aggregatenumber of transactions.

At Step 614, if the data sorting module 311 determines that a specifictenor group has been allocated fewer transactions (or less than a totaltransaction volume) than the minimum number, or the entire datacollection period (e.g., 5 days) has fewer than the target aggregatevolume and/or number of transactions, the data verification module 309may allocate eligible transaction data executed during the preceding dayor days (e.g., 5 days) to reach the minimum value. If the volume and/ornumber of transactions for the tenor group is still less that the targetvalue, or the volume and/or number of transactions for the entire datacollection period is still below that the target value, eligibletransaction data from the next preceding day may also be allocated toreach the minimum value. This procedure may continue until one of thefollowing conditions is met: the minimum number or volume oftransactions is reached or a maximum number of lookback days is exceeded(e.g., 10 days). After the maximum number of days is exceeded, acontingency procedure may be applied to generate a value. Additionaladjustments may apply in the event of policy rate changes or exceptionalmarket circumstances.

Next, at Step 616, once the above conditions are met, the dataadjustment module 317 may adjust the wholesale primary market fundingtransaction data and the secondary market bond transaction data. Theadjustments applied to the wholesale primary market funding transactiondata and the secondary market bond transaction data may be similar ordifferent. In one example, for secondary market bond transaction data,the data processing module 312 may convert yields to an annualized moneymarket basis.

In another example, the data adjustment module 317 may assign aweighting to each eligible wholesale primary market funding transactionand each eligible secondary market bond transaction. In one example,this may be a weighting of 100 percent to each eligible wholesaleprimary market funding transaction and a weighting of 50 percent to eacheligible secondary market bond transaction. In another example, the sameweighting may be applied to each transaction data type.

It should be noted that different weights are considered and may includeany combination of weighting. For example, a three-tiered weightingsystem may be used for the different types of inputs. To illustrate, aweighting of 100 percent may applied to all wholesale primary marketfunding transaction data, a 25 percent weight may be applied tosecondary market bond transactions with volumes greater than USD $5M,and a 10 percent weight may be applied to secondary market bondtransactions with volumes between USD $2M and USD $5M.

In an example, transaction data from earlier days in a data collectionperiod, or from days prior to the collection period, where required tomeet minimum thresholds, may be assigned the same weight as the currentday's data. In another example, the transaction data from earlier daysmay be assigned a reduced weighting relative to the current day'stransactions. In yet another example, the transaction data from earlierdays may also be adjusted by reference to movements in market rates(e.g., term nearly risk-free rates or overnight index swaps (“OIS”)since the date of execution). In some examples, weightings and otheradjustments may be applied cumulatively. For example, an eligible bondtransaction may be adjusted by multiplying by its weighting relative tofunding transactions and by further multiplying by an OIS adjustmentfactor.

Eligible bond transactions may also be weighted, where necessary ordesired, to ensure that no single bond issuer represents over athreshold percentage (e.g., 10 percent) of the bond transactions used toconstruct the yield curve for any given day. If, on any givencalculation day, there are fewer than a certain number (e.g., ten (10))issuers of bonds represented in the yield curve, then the thresholdpercentage may be increased to (100÷number of issuers) percent.

The eligible bond transactions weighting process described above mayutilize an iterative approach. For example, in Step 1 of an iterativeapproach, each issuer may be assigned a “token count” equal to thenumber of eligible bond transactions in respect of that issuer that areused to calculate the yield curve for a given calculation day. In Step2, a maximum token count any individual issuer is permitted for a givencalculation day may be determined as: (threshold percentage×aggregate ofthe token counts across all issuers for that day) rounded down to thenearest whole number (≥1). In Step 3, if the token count for anyindividual issuer exceeds this maximum token count, that issuer may beassigned a reduced token count equal to the maximum (if not, noweighting process is necessary). In Step 4, if a reduction occurs forany issuer, Steps 1-3 may be repeated. In Step 5, once no reductionoccurs after repeating Steps 1-3, the weight for each transaction ofeach issuer may be set as: reduced token count for that issuer dividedby original token count for that issuer. The weighting may be one (1)for issuers that were not subject to a reduction; and <1 for those thatwere. All bond transaction data may be converted to an annualized moneymarket basis.

At Step 618, the data processing module 312 may generate a yield curveusing the filtered, sorted and adjusted primary market fundingtransaction data and secondary market bond transaction data. The yieldcurve may be plotted for display via, for example, a graphical userinterface. The yield curve may be constructed using, for example, arobust regression best fit of all eligible data points.

At Step 620, the values for the publication tenors (e.g., one-month,three-month, and six-month) for the calculation day (excluding, forexample, any tenor group which has insufficient eligible transactiondata) may be identified from the yield curve at specified points on theyield curve. For example, these points may be the 30, 91 and 182days-to-maturity points on the yield curve. For tenor groups and/orcollection periods with insufficient eligible transaction data, acontingency policy may apply. At Step 622, the yield curve and thevalues may be transmitted to the one or more remote devices 107 via thedata distribution device 105. In an example, the yield curves and thevalues may be transmitted to the one or more remote devices 107 at a setfrequency, such as daily. The transmission of the yield curves and thevalues may include publishing this information to one or more websitesfor display via the Internet.

Referring now to FIG. 7, a first example of how yield curve and valuesfor publication tenors may be generated by the process of FIG. 6 isshown. In this example, a first bank yield curve may be generated. Thefirst bank yield curve may measure the average yield at which investorsare willing to invest U.S. dollar funds over one or more periods of time(e.g., one-month, three-month, and six-month periods) on a senior,unsecured basis in large, internationally active banks operating in thewholesale U.S. dollar markets for a specified time horizon (e.g., up toone year). The first bank yield curve may be generated through a processof curve-fitting a number of eligible transaction data points. Thex-axis of the bank yield curve may be days to maturity and the y-axismay be an annualized yield percentage.

The transactional data points may be sourced from Day T, Day T−1, DayT−2, Day T−3, and Day T−4, and may be used to derive the bank yieldcurve value for Day T and published on day T+1. From these transactionaldata points, a best-fit yield curve may be constructed from whichone-month, three-month and six-month BYI settings (representing valuesfor different tenors) may be determined. In the example shown in FIG. 7,355 transactional data points are used. Table 4 shows the transactions:

TABLE 4 Transactions Source Day Funding Transactions Bond Transactions T60 11 T-1 46 17 T-2 62 33 T-3 40 9 T-4 54 23

An aggregate volume of the funding transactions may be, for example, USD$26.1 billion.

The values for the one-month, three-month and six-month term settingsmay be taken from the curve at the 30, 91, and 182 days to maturitypoints, respectively. These values are included in the Table 5, togetherwith a corresponding conventional benchmark interest rate index ratepublished on the same day.

TABLE 5 Values from First Bank Yield Curve Tenor Bank Yield Index (%)Conventional Benchmark (%) One-Month 2.48142 2.48188 Two-Month 2.599112.59850 Three-Month 2.69339 2.68213

Referring now to FIGS. 8-10, another example yield curve and values forpublication tenors generated by the process of FIG. 6 is shown. In thisexample, a second bank yield curve may be generated by combining a termnearly risk-free rate curve and a credit spread curve fitted to a numberof implied credit spread data points, which may be derived bysubtracting a value on the term nearly risk-free rate curve at a giventime for the relevant maturity from each transaction yield data point.It should be noted, any other suitable processing criteria, includingthose described herein at Step 602 to Step 622 (e.g., receipt,filtration, storage, retrieval, application of eligibility criteria,sorting, adjustment and yield curve generation) may be utilized inrespect of other input data according to the particular implementation,such as, for example, processing data from derivatives referencingnearly risk-free rates and historical nearly risk free-rate data.

Using this approach, each transaction input data point may be convertedto reflect the implied credit spread of the relevant transaction yieldover a notional term “risk free” rate curve (e.g. a SOFR curve) bysubtracting the value on the term nearly risk-free rate curve at a giventime for the relevant maturity from each transaction yield data point.The resulting converted “credit spread” inputs may then be used togenerate a fitted-curve representing borrowing spreads only (i.e., acredit spread curve). This may be added back to the notional “risk free”rate curve (e.g., the implied term SOFR curve at a certain point intime) in order to produce a composite bank yield curve and theassociated BYI values.

This approach acknowledges the different underlying dynamics of thecredit and rates markets. Credit trends generally evolve over longertime periods, with interest rate expectations potentially changing morerapidly based upon either realized rate changes or changes inexpectations regarding monetary policy. Separating the yield curve intodiscrete parts allows for separate methodologies to be used for thecredit risk and nearly risk-free rate components of the transactionyield data reflecting these dynamics. The credit-spread curvemethodology may more closely model the movement of credit-sensitivetransaction data over the transaction window (e.g., 5 days) whencompared with a combined curve. In addition, the term nearly risk-freerate curve may incorporate more granular information on daily marketrate adjustments (for example, based on realized risk free rate (e.g.,SOFR) data and derivatives transactional data relating to expectedfuture risk free rate (e.g., SOFR) settings).

The second bank yield curve and BYI values may be representative ofsenior, unsecured bank credit risk in the wholesale funding market overtime. This approach may also offer the possibility of publishing thecredit spread and term nearly risk-free rate elements of the second bankyield curve separately, giving market participants greater transparencyas to the constituent elements of the second bank yield curve, while atthe same time retaining a nexus to an overnight nearly risk-freereference rate.

In this example, primary market funding and secondary market bondtransaction data points are sourced, filtered, weighted and normalizedin the same manner as for the first bank yield curve described above.However, the calculation process may incorporate one or more of thefollowing changes.

First, a term nearly risk-free rate (e.g., SOFR) yield curve may beconstructed from information that may include historical nearlyrisk-free rate data and data in respect of derivatives referencingnearly risk-free rate data. There may be no day-on-day adjustment formovements in market rates during the collection window. Instead, thevalue on the term nearly risk-free rate (e.g. SOFR) curve at a giventime for a relevant maturity may be subtracted from each transactionyield data point to generate implied credit spreads for eachtransaction. Next, a curve may be fitted to the implied credit spreadsrather than to the transaction yields themselves. This fitted creditspread curve may be added to the current term nearly risk-free rate(e.g., SOFR) yield curve to produce the second bank yield curve fromwhich the required one-month, three-month and six-month BYI values maybe obtained.

FIG. 8 shows an example of how an implied credit spread is derived foreach transaction, based on its vertical distance from the term nearlyrisk-free rate (e.g., SOFR) curve for the same day. The x-axis may bedays to maturity and the y-axis may be an annualized yield percentage.Term nearly risk-free rate (e.g., SOFR) rates for each applicableeffective day may be subtracted from each of the yields associated withthe wholesale, senior, unsecured transaction data points for that day inorder to produce implied credit spreads for each transaction. An impliedcredit spread may be derived for each transaction, based on its verticaldistance from the term nearly risk-free rate (e.g., SOFR) curve for thesame day. The arrow indicates the implied credit spread for a singletransaction on Day T. The transactions for each day are color-coded (orshaded) to match the implied term nearly risk-free rate (e.g., SOFR)yield curve for the same day.

As shown in FIG. 9, these implied credit spreads, taken together on arolling five-day basis, may be plotted on a chart having an x-axis ofdays to maturity and a y-axis of credit spread (bps). A robustregression algorithm may be applied to generate a credit-spread curve.The one-month, three-month, and six-month credit-spread settings maythen be determined from this curve. The arrow on the chart indicates thecredit spread derived for the same transaction highlighted in FIG. 8.

FIG. 10 shows how the implied credit spread curve can be added to theterm nearly risk-free rate (e.g., SOFR) curve to construct a creditsensitive yield curve (i.e., the second bank yield curve) from whichone-month, three-month and six-month BYI values may be obtained. Thesecond bank yield curve may have an x-axis of days to maturity and ay-axis of annualized yield percentage.

The second bank yield curve may be generating using the same 355transaction data points described above with reference to Table 4. Thenotional term nearly risk-free rate (e.g., SOFR) curve may be generatedbased on realized nearly risk-free (e.g., SOFR) rates and nearlyrisk-free rate (e.g., SOFR) futures settlement prices. The values forthe one-month, three-month and six-month term settings may be taken fromthe curve at the 30, 91, and 182 days to maturity points, respectively.These values are included in Table 6, together with the correspondingconventional benchmark interest rate index rate published on the sameday.

TABLE 6 Values from Second Bank Yield Curve Tenor Bank Yield Index (%)Conventional Benchmark (%) One-Month 2.48895 2.48188 Two-Month 2.597342.59850 Three-Month 2.68935 2.68213

The second bank yield curve may also be shown in its component partsderived through this alternative methodology. The term nearly risk-freerate (e.g., term SOFR) can be separated from the credit-sensitivesupplement (i.e., the credit spread) as illustrated in Table 7 below.

TABLE 7 Components of Second Bank Yield Curve Bank Yield Index Term SOFRCredit Spread Tenor (%) Component (%) Component (bps) One-Month 2.488952.47476 1.4 Two-Month 2.59734 2.46223 13.5 Three-Month 2.68935 2.4632122.6

This approach may allow end-users to have greater transparency regardingthe economic drivers behind the constituent elements of the second bankyield curve. In addition, it may also allow for the use of a creditspread component as a supplement in lending transactions that userealized compounded nearly risk-free rates (e.g., SOFR), whereappropriate.

Actual transaction rates/yields/credit spreads may vary, even fortransaction data having the same time to maturity/time to bond maturityfor the same bank that are executed on the same day. Accordingly, thecurve may not simply be drawn through the known data points. Rather, asingle curve may need to be fitted to the known data points for a givenday using a methodology that best represents the range of eligibletransaction rates/yields at each applicable maturity point.

The selection of a curve-fitting approach may depend, in part, onassumptions that may be reasonably made regarding the underlying dataand the resulting yield curve. For instance, a straight line, a simpleparabola, and an oscillating curve (e.g., a 6th order polynomial) mighteach be considered a “good fit” to the same data, depending on initialassumptions.

While the shape of a yield curve of the present disclosure may varyaccording to market conditions, it may be assumed to be a smoothcontinuous curve that does not oscillate. Two classes of curve-fittingalgorithms may be used. The first class is parametric, where the entirecurve is represented by a single function with a set of parameters thatdetermine its shape. The second class is spline-based, where a number oflocalized fitted curves are smoothly joined together.

In each case, curve-fitting may comprise a method of finding a “bestfit” curve that best represents the available data points (i.e.,minimizing some measure of net distance of the data points from thecurve). One approach may include determining a curve that minimizes theaverage (mean) of the squares of the vertical (y-axis) distances betweeneach data point and the curve (i.e., a “least squares” regression). Forexample, may be based on a parametric model, fitting to a third orderpolynomial (y=ax³+bx²+cx+d). This may allow the curve to have one or twoturning points and some variation in curvature, while still being arelatively simple function. In the case of a parametric model, a changeto any individual data point may affect the shape of the entire curve,but the curve itself will remain smooth. In the case of a spline-basedmodel, individual data points may have less impact on the shape of thewhole curve because each localized curve section is able to move largelyindependently of the others, but the resulting curve may be more likelyto oscillate.

In either a simple polynomial or a spline-based regression, outlier datapoints may potentially distort either part or the whole of the curve. Anoutlier exclusion approach, based on rejecting points located very farfrom an initial curve calculation, may help reduce their impact.However, any outlier exclusion approach may rely on setting anappropriate sensitivity range (with the intention being able to excludeonly unrepresentative points markedly different in value from aninitially calculated curve), so that the final curve does not deviatetoo far from the available market information and result in anunrepresentative index. For example, outliers may be excluded based ontheir (vertical) distance from the calculated curve (e.g., a +/−100 bpssensitivity).

A robust regression methodology (e.g., without outlier exclusion) mayalso be used to address outlier data points. This approach may usemultiple iterations to find a best-fit curve, with data points nearer tothe curve given the greatest weight to minimize the influence ofrelative outliers. In view of the possibility of occasional erroneousand unrepresentative transaction reports, this approach may also be usedin conjunction with a check for extreme outliers. This may be expectedto use a wide threshold (e.g., between 100 bps and 200 bps from theaverage for the relevant tenor) with the intention of excluding onlyclearly erroneous and unrepresentative transaction reports.

In the event that insufficient transaction data points are available toproduce the first bank yield curve or the second bank yield curve (e.g.,to generate the one-month, three-month and six-month settings, as mightbe the case during a period of market illiquidity), a contingency planmay be enacted. For example, the data structure management system 100may publish the settings that were last determined based upontransaction input data using the applicable BYI methodology, adjustedfor movements in “risk free” rates (e.g. OIS, U.S. treasury yields,implied term SOFR rates, etc.). This may allow for the continuedpublication of the yield curves and BYI during periods of marketilliquidity. These contingency settings may incorporate both the mostrecently available eligible credit sensitive transaction data (i.e., themost recent BYI setting derived from the relevant transaction datainputs using the BYI methodology as opposed to the contingency policy)and “risk free” rates data in order to ensure the contingency rates arerepresentative of, and responsive to, market conditions at the time. TheBYI may resume publication in accordance with the methodologiesdisclosed herein as soon as enough primary market funding and/orsecondary market bond transaction/and or other necessary/suitable inputdata points become available.

Referring now to FIG. 11, a functional block diagram illustrating anexample computer system 1100 is shown. The computer system 1100 may beused in one or more of the one or more data source devices 109, the datastructure management system 100, and the one or more remote devices 107described above. In some examples, the computer system 1100 may beconnected (e.g., networked) to other machines as described above. Thecomputer system 1100 may operate in the capacity of a server or a clientmachine in a client-server network environment, or as a peer machine ina peer-to-peer (or distributed) network environment. The computer system1100 may be any special-purpose machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine for performing the functions describe herein.

Further, while only a single computer system 1100 is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein.

The example computer system 1100 may include processing device 1102,memory 1106, data storage device 1110 and communication interface 1112,which may communicate with each other via data and control bus 1118. Insome examples, computer system 1100 may also include display device 1114and/or user interface 1116.

Processing device 1102 may include, without being limited to, amicroprocessor, a central processing unit, an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), a network processor and/or a suitablespecial-purpose processing device specially programmed with processinglogic 1104 to perform the operations described herein.

Memory 1106 may include, for example, without being limited to, aread-only memory (ROM), a random access memory (RAM), a flash memory, adynamic RAM (DRAM), a static RAM (SRAM) and/or a suitable non-transitorycomputer readable storage medium storing computer-readable instructions1108 executable by processing device 1102 for performing the operationsdescribed herein. Although one memory device 1108 is illustrated in FIG.11, in some examples, computer system 1100 may include two or morememory devices (e.g., dynamic memory and static memory).

Computer system 1100 may include communication interface device 1112,for direct communication with other computers (including wired and/orwireless communication) and/or for communication with a network. In someexamples, computer system 1100 may include display device 1114 (e.g., aliquid crystal display (LCD), a touch sensitive display, etc.). In someexamples, computer system 1100 may include user interface 1116 (e.g., analphanumeric input device, a cursor control device, etc.).

In some examples, computer system 1100 may include data storage device1110 storing instructions (e.g., software) for performing any one ormore of the functions described herein. Data storage device 1110 mayinclude any suitable non-transitory computer-readable storage medium,including, without being limited to, solid-state memories, optical mediaand magnetic media.

For purposes of this disclosure, the term “computer” shall refer to anelectronic device or devices, including those specifically configuredwith capabilities to be utilized in connection with a data conversionand distribution system according to the present disclosure, such as adevice capable of receiving, transmitting, processing and/or using dataand information in the particular manner and with the particularcharacteristics described herein. The computer may include a server, aprocessor, a microprocessor, a personal computer, such as a laptop, palmPC, desktop or workstation, a network server, a mainframe, an electronicwired or wireless device, such as for example, a telephone, a cellulartelephone, a personal digital assistant, a smartphone, an interactivetelevision, such as for example, a television adapted to be connected tothe Internet or an electronic device adapted for use with a television,an electronic pager or any other computing and/or communication devicespecifically configured to perform one or more functions describedherein.

The term “network” shall refer to one or more networks, including thosecapable of being utilized in connection with a data conversion anddistribution system described herein, such as, for example, any publicand/or private networks, including, for instance, the Internet, anintranet, or an extranet, any wired or wireless networks or combinationsthereof.

The term “user interface” shall refer to a suitable type of device,connection, display and/or system through which information may beconveyed to and received from a user in accordance with the presentdisclosure, such as, without limitation, a monitor, a computer, agraphical user interface, a terminal, a screen, a keyboard, atouchscreen, a biometric input device that may include a microphoneand/or camera, a telephone, a personal digital assistant, a smartphone,or an interactive television.

The term “computer-readable storage medium” should be taken to include asingle medium or multiple media that store one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium that is capable of storing or encoding a setof instructions for execution by the machine and that causes the machineto perform any one or more of the methodologies of the presentdisclosure.

The term “or” may be construed in an inclusive or exclusive sense.Similarly, the term “for example” may be construed merely to mean anexample of something or an exemplar and not necessarily a preferredmeans of accomplishing a goal.

As described above, examples of the present disclosure relate to datastructure management systems and methods for data isolation and thecreation of data metrics such as a yield index, a bank yield index, etc.The data structure management systems and methods of the presentdisclosure may isolate correct data from among one or more data sources,and convert the isolated data into one or more data metrics such as,without being limited to, a bank yield index. Systems and methods of thepresent disclosure are operationally efficient (e.g., by isolating,analyzing, adjusting and appropriately and pertinently processing onlythe correct data) and may result in the creation of more accurate datametrics (e.g., through analysis of only the correct, isolated data).

Moreover, the data structure management systems provide technicalimprovements over conventional systems and techniques. This is becausethe data structure management systems of the present disclosure includean unconventional technique of isolating and appropriately andpertinently using only the correct data among data obtained from amongone or more networked data sources. The unconventional technique is ableto isolate correct data even when the data sources provide sparse orconcentrated data sets. The ability to isolate (e.g., filter) only thecorrect data even in sparse/concentrated data sets and appropriately andpertinently process this data does not exist in conventionalsystems/techniques and, thus, conventional systems/techniques mayproduce inaccurate and unreliable or inappropriate data metrics.

While the present disclosure has been discussed in terms of certainexamples, it should be appreciated that the present disclosure is not solimited. The embodiments are explained herein by way of example, andthere are numerous modifications, variations and other embodiments thatmay be employed that would still be within the scope of the presentinvention.

1. A system for isolating electronic data and generating enhanced data,the system comprising: a data subscription unit having at least one datainterface communicatively coupled to one or more data source devices,the data subscription unit configured to: receive data having aplurality of data formats from the one or more data source devices,and/or transmit the data to one or more storage databases via a securecommunication channel over at least one network; a modeling unitcomprising one or more servers, a non-transitory memory, and one or moreprocessors comprising machine readable instructions, the modeling unitcommunicatively coupled to the data subscription unit, the modeling unitfurther comprising: a data receiver module configured to receive thedata from the one or more storage databases via the secure communicationover the at least one network, a data verifier configured to determineeligibility of the stored data based on one or more eligibilitycriteria, a data sorting module configured to sort the eligible datainto one or more groups, the sorting based on at least one sortingparameter or a minimum aggregate value for at least one attribute of theeligible data, a data adjustment module configured to adjust the sortedeligible data based on one or more adjustment parameters, and a dataprocessing module configured to: generate data metrics over a definedset of time periods based on the adjusted eligible data, derive furtherdata metrics based on the data metrics, and/or derive specified valuesfrom among the data metrics and the further data metrics; and a datadistribution device configured to transmit or make available the datametrics, the further data metrics, and the values to one or more remotedevices over the at least one network.
 2. The system of claim 1, whereinthe data verifier is further configured to retrieve the one or moreeligibility criteria from one or more reference databases.
 3. The systemof claim 1, wherein the at least one sorting parameter comprises a timeto maturity of the stored data.
 4. The system of claim 1, wherein datasubscription unit is further configured to: receive the data having theplurality of data formats pre-filtered from the one or more data sourcedevices; and filter the data having the plurality of data formatspre-filtered based on one or more filter criteria, the filter criteriacomprising at least one of a transaction data and a value date.
 5. Thesystem of claim 4, wherein the data sorting module is further configuredto: determine that a minimum number and/or volume of data points of atleast a first group of the one or more groups is not met; and addadditional data to the at least the first group to meet the minimumnumber and/or volume of data points.
 6. The system of claim 5, where thedata adjustment module is further configured to apply one or moreweights to at least a portion of the data points.
 7. The system of claim1, wherein the data having a plurality of data formats comprises, atleast, wholesale primary market funding transaction data and secondarymarket bond transaction data, said data being received during one ormore particular time periods.
 8. The system of claim 7, wherein the dataadjustment module is further configured to assign the wholesale primarymarket funding transaction data at least a first weight and assign thesecondary market bond transaction data at least a second weight.
 9. Thesystem of claim 1, wherein the one or more eligibility criteriacomprises one or more of: transaction type, counterparty type, fundinglocation, maturity range, minimum transaction size, obligation type,maturity range, issuance size, and coupon range, depending on the datatype.
 10. The system of claim 1, further comprising: a surveillancemodule configured to perform one or more of surveillance, administrationand supervision of the modeling unit contemporaneously with the datacollection and calculation, and/or post-publication, the surveillancemodule further configured to receive one or more inputs.
 11. The systemof claim 6, wherein the data metrics comprise a first bank yield curveand further data metrics comprise a second bank yield curve.
 12. Thesystem of claim 11, wherein the data processing module is furtherconfigured to generate the first bank yield curve by: charting theweighted, adjusted and/or sorted eligible data based on annualized yieldand days to maturity; performing a check for outlier data points; andcreating a curve using a curve-fitting methodology comprising one ormore of a least squares best fit to a third polynomial order and arobust regression.
 13. The system of claim 11, wherein the dataprocessing module is further configured to derive the specified valuesby extrapolating data points at each period of time represented by theone or more groups from the among the first bank yield curve and thesecond bank yield curve.
 14. The system of claim 1, wherein the one ormore groups comprise one or more tenor groups representing, at least, aone month period, a three month period, and a six month period.
 15. Thesystem of claim 6, wherein the data adjustment module is furtherconfigured to normalize the sorted eligible data responsive to one ormore of: a bond issuer representing more than a threshold percentage ofbond transactions, changes in market rates, and conversions betweendifferent price/yield quotation conventions.
 16. A method for isolatingelectronic data and generating enhanced data, the method comprising:receiving, by a data subscription unit having at least one datainterface communicatively coupled to one or more data source devices,data having a plurality of data formats from the one or more data sourcedevices; transmitting, by the data subscription unit, the data to one ormore storage databases via a secure communication channel over at leastone network; receiving, by a data receiver module of a modeling unitcommunicatively coupled to the data subscription unit, the data from theone or more storage databases via the secure communication over the atleast one network, the modeling unit comprising one or more servers, anon-transitory memory, and one or more processors comprising machinereadable instructions; determining, by a data verifier of the modelingunit, eligibility of the stored data based on one or more eligibilitycriteria; sorting, by a data sorting module of the modeling unit, theeligible data into one or more groups, the sorting based on at least onesorting parameter or a minimum aggregate value for at least oneattribute of the eligible data; adjusting, by a data adjustment module,the sorted eligible data based on one or more adjustment parameters;generating, by the data processing module, data metrics over a definedset of time periods based on the adjusted eligible data; deriving, bythe data processing module, further data metrics based on the datametrics; deriving, by the data processing module, specified values fromamong the data metrics and the further data metrics; and transmitting ormaking available, by a data distribution device, the data metrics, thefurther data metrics, and the values to one or more remote devices overthe at least one network.
 17. The method of claim 16, furthercomprising: retrieving, by the data verifier, the one or moreeligibility criteria from one or more reference databases.
 18. Themethod of claim 16, wherein the at least one sorting parameter comprisesa time to maturity of the stored data.
 19. The method of claim 16,further comprising one or more of: receiving, by the data subscriptionunit, the data having the plurality of formats pre-filtered data fromthe one or more data source devices; and filtering, by the datasubscription unit, the data having the plurality of formats based on oneor more filter criteria, the filter criteria comprising at least one ofa transaction date and a value date.
 20. The method of claim 19, furthercomprising: determining, by the data sorting module, that a minimumnumber and/or volume of data points of at least a first group of the oneor more groups is not met; and adding, by the data processing module,additional data to the at least the first group to meet the minimumnumber and/or volume of data points.
 21. The method of claim 20, furthercomprising: applying, by the data adjustment module, one or more weightsto at least a portion of the data points.
 22. The method of claim 16,wherein the data having a plurality of data formats comprises, at least,wholesale primary market funding transaction data and secondary marketbond transaction data, said data being received during one or moreparticular time periods.
 23. The method of claim 22, further comprising:assigning, by the data adjustment module, the wholesale primary marketfunding transaction data at least a first weight and assign thesecondary market bond transaction data at least a second weight.
 24. Themethod of claim 16, wherein the one or more eligibility criteriacomprises one or more of: transaction type, counterparty type, fundinglocation, maturity range, minimum transaction size, obligation type,maturity range, issuance size, a number of duplicate data points, andcoupon range, depending on data type.
 25. The method of claim 16,further comprising: performing, by a surveillance module, one or more ofsurveillance, administration and supervision of the modeling unitcontemporaneously with the data collection and calculation, and/orpost-publication, the surveillance module further configured to receiveone or more input.
 26. The method of claim 21, wherein the data metricscomprise a first bank yield curve and the further data metrics comprisea second bank yield curve.
 27. The method of claim 26, furthercomprising: generating, by the data processing module the first bankyield curve by charting the weighted, adjusted and/or sorted eligibledata based on annualized yield and days to maturity; checking, by thedata processing module, for outlier data points; and creating, by thedata processing module, a curve using a curve-fitting methodologycomprising one or more of a least squares best fit to a third polynomialorder and a robust regression.
 28. The method of claim 26, furthercomprising: deriving, by the data processing module, the specifiedvalues by extrapolating data points at each period of time representedby the one or more groups from among the first bank yield curve and thesecond bank yield curve.
 29. The method of claim 16, wherein the one ormore groups comprise one or more tenor groups representing, at least, aone month period, a three month period, and a six month period.
 30. Themethod of claim 21, wherein said adjusting further comprises normalizingthe sorted eligible data responsive to one or more of: a bond issuerrepresenting more than a threshold percentage of bond transactions,changes in market data, and conversions between different price/yieldquotation conventions.